RoboDexVLM：基于VLM分层架构的通用灵巧机器人操作

Core Insights - RoboDexVLM is an innovative robot task planning and grasp detection framework designed for collaborative robotic arms equipped with dexterous hands, focusing on complex long-sequence tasks and diverse object manipulation [2][6] Group 1: Framework Overview - The framework utilizes a robust task planner with a task-level recovery mechanism, leveraging visual language models to interpret and execute open vocabulary instructions for completing long-sequence tasks [2][6] - It introduces a language-guided dexterous grasp perception algorithm, specifically designed for zero-shot dexterous manipulation of diverse objects and instructions [2][6] - Comprehensive experimental results validate RoboDexVLM's effectiveness, adaptability, and robustness in handling long-sequence scenarios and executing dexterous grasping tasks [2][6] Group 2: Key Features - The framework allows robots to understand natural language commands, enabling seamless human-robot interaction [7] - It supports zero-shot grasping of various objects, showcasing the dexterous hand's capability to manipulate items of different shapes and sizes [7] - The visual language model acts as the "brain" for long-range task planning, ensuring that the robot does not lose track of its objectives [7] Group 3: Practical Applications - RoboDexVLM represents the first general-purpose dexterous robot operation framework that integrates visual language models, breaking through the limitations of traditional and end-to-end methods [6][7] - The framework's real-world performance demonstrates its potential in embodied intelligence and human-robot collaboration [6][7]